Digital imagery has become commonplace in recent years, and cameras are commonly included in mobile phones, laptops, security systems and the like. Huge amounts of image data are thus being captured, and, as such, it can be difficult to efficiently analyse the data.
Automatic image analysis can be used to process large amounts of data within a short time, and has thus been the topic of many research projects. Object detection, and in particular facial detection, is an important tool in automatic image analysis as it enables analysis of a structure of an image.
The use of active appearance models (AAMs) is popular in facial detection applications, and uses a statistical model that is built using training data to detect a face in an image. An AAM is matched to a face using a shape and appearance of the face. AAMs can be used to model types of objects other than faces, but each model is typically specific to a certain type of object.
A problem with AAMs of the prior art is that they do not work well with appearance variations that are not present in the training data, or when large illumination variation or occlusion is present. Furthermore, AAMs are generally complex.
Constrained local models (CLMs) provide certain improvements over AAMs with respect to some of the problems described above. A CLM is fitted to an object by locating sets of points on a target image, wherein the sets of points are constrained by a statistical shape model.
A problem with CLMs of the prior art is that the modelling process can be complex and time consuming. Attempts at reducing complexity can result in a system that is not sufficiently robust and/or wherein the model is not accurate. Similarly, such systems are not particularly suited to complexity-sensitive applications such as real time and mobile applications.
Accordingly, there is a need for an improved system and method of tracking a non-rigid object.